Doctor of Philosophy

dissertationDue to the popularity of Web 2.0 and Social Media in the last decade, the percolation of user generated content (UGC) has rapidly increased. In the financial realm, this results in the emergence of virtual investing communities (VIC) to the investing public. There is an on-going debate among scholars and practitioners on whether such UGC contain valuable investing information or mainly noise. I investigate two major studies in my dissertation. First I examine the relationship between peer influence and information quality in the context of individual characteristics in stock microblogging. Surprisingly, I discover that the set of individual characteristics that relate to peer influence is not synonymous with those that relate to high information quality. In relating to information quality, influentials who are frequently mentioned by peers due to their name value are likely to possess higher information quality while those who are better at diffusing information via retweets are likely to associate with lower information quality. Second I propose a study to explore predictability of stock microblog dimensions and features over stock price directional movements using data mining classification techniques. I find that author-ticker-day dimension produces the highest predictive accuracy inferring that this dimension is able to capture both relevant author and ticker information as compared to author-day and ticker-day. In addition to these two studies, I also explore two topics: network structure of co-tweeted tickers and sentiment annotation via crowdsourcing. I do this in order to understand and uncover new features as well as new outcome indicators with the objective of improving predictive accuracy of the classification or saliency of the explanatory models. My dissertation work extends the frontier in understanding the relationship between financial UGC, specifically stock microblogging with relevant phenomena as well as predictive outcomes

Men, Women, Microblogging: Where Do We Stand?

With millions of users worldwide, microblogging has developed into a powerful tool for interaction and information dissemination. While both men and women readily use this technology, there are significant differences in how they embrace it. Understanding these differences is important to ensure gender parity, provide advertisers with actionable insights on the marketing potential of both groups, and to inform current theories on how microblogging affordances shape gender roles. So far, existing research has not provided a unified framework for such analysis, with gender insights scattered across multiple studies. To fill this gap, our study conducts a comprehensive meta-review of existing research. We find that current discourse offers a solid body of knowledge on gender differences in adoption, shared content, stylistic presentation, and a rather convoluted picture of female and male interaction. Together, our structured findings offer a deeper insight into the underlying dynamics of gender differences in microblogging

Leveraging Twitter data to analyze the virality of Covid-19 tweets: a text mining approach

As the novel coronavirus spreads across the world, work, pleasure, entertainment, social interactions, and meetings have shifted online. The conversations on social media have spiked, and given the uncertainties and new policies, COVID-19 remains the trending topic on all such platforms, including Twitter. This research explores the factors that affect COVID-19 content-sharing by Twitter users. The analysis was conducted using 57,000 plus tweets that mentioned COVID-19 and related keywords. The tweets were subjected to the Natural Language Processing (NLP) techniques like Topic modelling, Named Entity-Relationship, Emotion & Sentiment analysis, and Linguistic feature extraction. These methods generated features that could help explain the retweet count of the tweets. The results indicate that tweets with named entities (person, organisation, and location), expression of negative emotions (anger, disgust, fear, and sadness), reference to mental health, optimistic content, and greater length have higher chances of being shared (retweeted). On the other hand, tweets with more hashtags and user mentions are less likely to be shared

Misinformation Detection in Social Media

abstract: The pervasive use of social media gives it a crucial role in helping the public perceive reliable information. Meanwhile, the openness and timeliness of social networking sites also allow for the rapid creation and dissemination of misinformation. It becomes increasingly difficult for online users to find accurate and trustworthy information. As witnessed in recent incidents of misinformation, it escalates quickly and can impact social media users with undesirable consequences and wreak havoc instantaneously. Different from some existing research in psychology and social sciences about misinformation, social media platforms pose unprecedented challenges for misinformation detection. First, intentional spreaders of misinformation will actively disguise themselves. Second, content of misinformation may be manipulated to avoid being detected, while abundant contextual information may play a vital role in detecting it. Third, not only accuracy, earliness of a detection method is also important in containing misinformation from being viral. Fourth, social media platforms have been used as a fundamental data source for various disciplines, and these research may have been conducted in the presence of misinformation. To tackle the challenges, we focus on developing machine learning algorithms that are robust to adversarial manipulation and data scarcity. The main objective of this dissertation is to provide a systematic study of misinformation detection in social media. To tackle the challenges of adversarial attacks, I propose adaptive detection algorithms to deal with the active manipulations of misinformation spreaders via content and networks. To facilitate content-based approaches, I analyze the contextual data of misinformation and propose to incorporate the specific contextual patterns of misinformation into a principled detection framework. Considering its rapidly growing nature, I study how misinformation can be detected at an early stage. In particular, I focus on the challenge of data scarcity and propose a novel framework to enable historical data to be utilized for emerging incidents that are seemingly irrelevant. With misinformation being viral, applications that rely on social media data face the challenge of corrupted data. To this end, I present robust statistical relational learning and personalization algorithms to minimize the negative effect of misinformation.Dissertation/ThesisDoctoral Dissertation Computer Science 201

Sentiment analysis and real-time microblog search

This thesis sets out to examine the role played by sentiment in real-time microblog search. The recent prominence of the real-time web is proving both challenging and disruptive for a number of areas of research, notably information retrieval and web data mining. User-generated content on the real-time web is perhaps best epitomised by content on microblogging platforms, such as Twitter. Given the substantial quantity of microblog posts that may be relevant to a user query at a given point in time, automated methods are required to enable users to sift through this information. As an area of research reaching maturity, sentiment analysis offers a promising direction for modelling the text content in microblog streams. In this thesis we review the real-time web as a new area of focus for sentiment analysis, with a specific focus on microblogging. We propose a system and method for evaluating the effect of sentiment on perceived search quality in real-time microblog search scenarios. Initially we provide an evaluation of sentiment analysis using supervised learning for classi- fying the short, informal content in microblog posts. We then evaluate our sentiment-based filtering system for microblog search in a user study with simulated real-time scenarios. Lastly, we conduct real-time user studies for the live broadcast of the popular television programme, the X Factor, and for the Leaders Debate during the Irish General Election. We find that we are able to satisfactorily classify positive, negative and neutral sentiment in microblog posts. We also find a significant role played by sentiment in many microblog search scenarios, observing some detrimental effects in filtering out certain sentiment types. We make a series of observations regarding associations between document-level sentiment and user feedback, including associations with user profile attributes, and users’ prior topic sentiment

Examining the Impact of Emojis on Disaster Communication: A Perspective from the Uncertainty Reduction Theory

Communication is a purposeful process, especially during disasters, when emergency management officials and citizen journalists attempt to disseminate relevant information to as many affected people as possible. X (previously Twitter), a popular computer-mediated communication (CMC) platform, has become an essential resource for disaster information given its ability to facilitate real-time communication. Past studies on disasters have mainly concentrated on the verbal-linguistic conventions of words and hashtags as the means to convey disaster-related information. Little attention has been given to non-verbal linguistic cues, such as emojis. In this study, we investigate the use of emojis in disaster communication on X by using uncertainty reduction theory as the theoretical framework. We measured information uncertainty in individual tweets and assessed whether information conveyed in external URLs mitigated such uncertainty. We also examined how emojis affect information uncertainty and information dissemination. The statistical results from analyzing tweets related to the 2018 California Camp Fire disaster show that information uncertainty has a negative impact on information dissemination, and the negative impact was amplified when emojis depicted items and objects instead of facial expressions. Conversely, external URLs reduced the negative impact. This study sheds light on the influence of emojis on the dissemination of disaster information on X and provides insights for both academia and emergency management practitioners in using CMC platforms

Microcelebrity Practices: A Cross-Platform Study Through a Richness Framework

Social media have introduced a contemporary shift from broadcast to participatory media, which has brought about major changes to the celebrity management model. It is now common for celebrities to bypass traditional mass media and take control over their promotional discourse through the practice of microcelebrity. The theory of microcelebrity explains how people turn their public persona into media content with the goal of gaining and maintaining audiences who are regarded as an aggregated fan base. To accomplish this, the theory suggests that people employ a set of online self-presentation techniques that typically consist of three core practices: identity constructions, fan interactions and promoting visibility beyond the existing fan base. Studies on single platforms (e.g., Twitter), however, show that not all celebrities necessarily engage in all core practices to the same degree. Importantly, celebrities are increasingly using multiple social media platforms simultaneously to expand their audience, while overcoming the limitations of a particular platform. This points to a gap in the literature and calls for a cross-platform study. This dissertation employed a mixed-methods research design to reveal how social media platforms i.e., Twitter and Instagram, helped celebrities grow and maintain their audience. The first phase of the study relied on a richness scoring framework that quantified social media activities using affordance richness, a measure of the ability of a post to deliver the information necessary in affording a celebrity to perform an action by using social media artifacts. The analyses addressed several research questions regarding social media uses by different groups of celebrities and how the audience responded to different microcelebrity strategies. The findings informed the design of the follow-up interviews with audience members. Understanding expectations and behaviors of fans is relevant not only as a means to enhance the practice’s outcome and sustain promotional activity, but also as a contribution to our understandings about contemporary celebrity-fans relationships mediated by social media. Three findings are highlighted. First, I found that celebrities used the two platforms differently, and that different groups of celebrities emphasized different core practices. This finding was well explained by the interviews suggesting that the audiences had different expectations from different groups of celebrities. Second, microcelebrity strategies played an important role in an audience’s engagement decisions. The finding was supported by the interviews indicating that audience preferences were based on some core practices. Lastly, while their strategies had no effect on follow and unfollow decisions, the consistency of the practices had significant effects on the decisions. This study makes contributions to the theory of Microcelebrity and offers practical contributions by providing broad insights from both practitioners’ and audiences’ perspectives. This is essential given that microcelebrity is a learned practice rather than an inborn trait